Evidence (11633 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
AI dissolves the boundaries that once separated firms, markets, experts, and consumers by internalizing human multimodal interfaces (language, vision, and behavioral data) into computational systems.
Theoretical argument and conceptual framework introduced in the paper (Structural Dissolution Framework); no empirical sample or quantitative analysis reported for this claim in the text provided.
Failures are structured by task family and execution surface, with HR, management, and multi-system business workflows as persistent bottlenecks and local workspace repair comparatively easier but unsaturated.
Error-mode analysis across the 105 tasks and evaluated models reported in experiments; authors identify task-family-level patterns (HR, management, multi-system workflows) and relative ease of local workspace repair.
Whether LLM-based assistants improve or degrade code quality remains unresolved: existing studies report contradictory outcomes contingent on context and evaluation criteria.
Review finds mixed/contradictory findings across included studies regarding code quality effects.
Architectural interventions can instead be used to trade off personalization against preference privacy.
Proposed solution described in the paper (architectural interventions) as an alternative to prompt-level fixes; presented as a design tradeoff rather than empirically validated mitigation in the excerpt.
AI-driven automation marks the beginning of a new political era—one in which the role of work in society becomes a central axis of welfare conflict.
Theoretical and interpretive claim in the paper, motivated by the survey findings and broader argumentation about political consequences.
The system tends to be factually correct when it answers but often omits information (i.e., 'the system is right when it answers — it just leaves things out').
Interpretation combining reported factual accuracy (85.5%) with low completeness (0.40) from benchmark results.
Differences between models are large enough to shape outcomes in practice, so reliability should be incorporated alongside average performance when assessing and deploying LLMs in high-stakes decision contexts.
Authors' interpretation of empirical differences in funding decisions, scores, confidence, and reliability across models in the controlled experiment; presented as an implication/recommendation.
This hybrid Make governance form has qualitatively different economics, capability requirements, and governance structures than pre-AI in-house development.
Paper's conceptual comparison between pre-AI hierarchy and post-AI hybrid Make governance (theoretical reasoning and examples; no empirical quantification).
AI reshapes seven canonical decision determinants for make-or-buy choices: cost, strategic differentiation, asset specificity, vendor lock-in, time-to-market, quality and compliance, and organizational capability.
Paper's factor-level conceptual analysis enumerating and discussing seven determinants (theoretical synthesis rather than empirical measurement).
Demographic characteristics intersect with AI exposure—i.e., exposure varies by demographic groups.
Paper reports that it examines how demographic characteristics intersect with exposure based on recent empirical studies; no demographic breakdowns or sample sizes provided in the abstract.
Recent studies combine task-level exposure metrics with employment and usage data to assess AI exposure and impacts.
Paper notes that it draws on studies that use task-level exposure metrics alongside employment and usage data; methodological claim rather than a quantitative result.
Generative large language models (LLMs) present organizations with a transformative technology whose labor market implications remain nascent yet consequential.
Statement in paper synthesizing emerging empirical research; no specific study, method, or sample size reported in the abstract.
The adoption of AI in Israel constitutes a systemic transformation of employment relations, necessitating doctrinal adaptation and institutional reform to keep the labor market aligned with foundational legal principles.
Synthesis and conclusion from the paper's combined legal and empirical analysis; presented as the author's overarching interpretive claim rather than as a specific quantified finding.
Within the public sector, there is an emerging policy trend to incorporate AI considerations into workforce planning, including examining whether human positions may be substituted by technological solutions prior to recruiting new employees.
Paper reports an observed policy trend in public-sector workforce planning; specific policy documents, jurisdictions, or counts not provided in the excerpt.
Objectives, constraints, and prompt guidance affect reliability and generalization.
Authors' analysis and discussion based on experiments and ablations described in the paper (qualitative/empirical observations about sensitivity to objectives, constraints, and prompts).
The architect's role is shifting, but the human remains central.
Authors' discussion and interpretive analysis about the role of humans in agentic AI-driven design processes.
Across evolved designs, components often correspond to known techniques; the novelty lies in how they are coordinated.
Authors' qualitative analysis of evolved architectures and components reported in the paper (design inspection and interpretation of evolved solutions).
The study establishes statistically significant relationships between organizational AI adoption and compensation dynamics.
Econometric estimates (difference-in-differences and propensity score matched comparisons) using the combined datasets listed in the paper and controlling for industry, firm size, geography, occupation characteristics, and macroeconomic variables.
The study establishes statistically significant relationships between organizational AI adoption and changes in occupational structures.
Same econometric approach (difference-in-differences and propensity score matching) applied to combined datasets (Anthropic Economic Index, Census Business Trends and Outlook Survey, Federal Reserve regional surveys, labor market analytics), with controls for industry, firm size, location, occupation-level characteristics, and macroeconomic environment.
The study establishes statistically significant relationships between organizational AI adoption and changes in employment patterns in the United States during 2022–2025.
Econometric analysis using multiple large-scale data sources (Anthropic Economic Index, U.S. Census Bureau Business Trends and Outlook Survey, Federal Reserve regional surveys, labor market analytics) and methods described as difference-in-differences estimation and propensity score matching controlling for industry (NAICS 2-digit), firm size, geography, occupation characteristics, and macro conditions.
We identify significant differences between human and AI negotiation behaviors, finding that humans favor lower-complexity deals and are significantly less reliable partners compared to LM-based agents.
Results from the user study comparing human vs LM-based agent negotiation behavior (statements in the results section).
High-value uses require broader authority exposure — data access, workflow integration, and delegated authority — when governance controls have not yet decoupled capability from authority exposure.
Conceptual/mechanism claim articulated in the paper (motivating assumption for the analytical model; no empirical sample given in the abstract).
Firms are deploying more capable AI systems, but organizational controls often have not kept pace.
Stated as background context in the paper's abstract/introduction (observational claim; no empirical sample or experiment reported in the abstract).
The distribution of complementary (non-AI) skills across the workforce shapes whether AI improvements generate productivity bottlenecks or concentration-driven inequality.
Derived from the task-based model analysis described in the article; framed as a theoretical mechanism with reference to empirical patterns but without specific empirical study details in the excerpt.
There is a strict policy reversal in optimal editorial policy sign: tightening is optimal pre-transition, loosening is optimal post-transition.
Analytical proof in the model showing the sign reversal of the editor's optimal constrained response as AI capability crosses the critical threshold.
After the AI transition, editors must loosen acceptance standards while investing in AI detection, because further tightening only amplifies dissipative polishing without improving sorting.
Analytical characterization of the constrained optimal editorial response in the post-transition regime within the model; argument relies on the discontinuous reviewer-effort collapse and comparative statics.
The reviewer-effort collapse creates a welfare misalignment: authors benefit from a weakened 'rat race' while editors suffer from degraded signal informativeness.
Comparative statics and welfare analysis in the theoretical model showing authors' equilibrium payoffs rise as competition/polishing dissipates, while editor's signal informativeness declines due to lower reviewer effort.
In academic peer review, generative AI enters both sides of the market: authors use AI to polish submissions, and reviewers use it to generate plausible reports without exerting evaluative effort.
Model assumption and motivation in the paper's three-sided equilibrium framework; described as the dual adoption mechanism analyzed analytically (no empirical sample size reported).
The paper extends paradox theory to conceptualise the Creativity Paradox in the context of GenAI.
Theoretical extension and conceptual development within the paper (no empirical tests reported).
Within that n=11 subset, 9 of 11 agents shift by at least 2 ranks between composite and benchmark-only rankings.
Comparison of rank positions between composite and benchmark-only rankings on the 11-agent subset; reported count of agents that moved at least 2 ranks.
The four factors capture largely complementary information (n=50; ρ_max = 0.61 for Adoption-Ecosystem, all others |ρ| ≤ 0.37).
Correlation analysis among the four factor scores computed on the 50-agent sample; reported maximum inter-factor Pearson/Spearman correlation coefficients.
The intervention only modestly narrows the gap to a full-information benchmark.
Comparison between post-intervention calibration/auction outcomes and a full-information benchmark reported in the paper, showing only modest improvement.
Provisioned Throughput delivers the lowest latency at low concurrency but saturates its reserved capacity above approximately 20 concurrent users.
Empirical measurements from the instrumented system across concurrency up to 50 users and tier comparisons; the paper reports the observed saturation point near ~20 concurrent users.
Delegating tasks to genAI can be individually beneficial in the short term even as widespread adoption degrades future model performance (creating a social dilemma).
Result of the paper's behavioral model showing an individual-level incentive to use genAI versus a collective cost from adoption (theoretical/model-based; no empirical sample reported in abstract).
Token usage is highly variable and inherently stochastic: runs on the same task can differ by up to 30x in total tokens.
Observed run-to-run variability in total token counts for identical tasks across the collected agentic trajectories from eight frontier LLMs on SWE-bench Verified.
ASC (adaptive stopping criterion) halts harmful refinement but incurs a 3.8 pp confidence-elicitation cost.
Reported experiment with ASC showing that it prevents harmful iterative refinement yet causes a measured cost described as 3.8 percentage points due to confidence elicitation.
Only o3-mini (+3.4 pp, EIR = 0%), Claude Opus 4.6 (+0.6 pp, EIR ~ 0.2%), and o4-mini (+/-0 pp) remain non-degrading under self-correction; GPT-5 degrades by -1.8 pp.
Reported measured changes in accuracy (percentage-point changes) and measured EIR values for the named models after applying iterative self-correction across the experiment suite.
Across 7 models and 3 datasets (GSM8K, MATH, StrategyQA), we find a sharp near-zero EIR threshold (<= 0.5%) separating beneficial from harmful self-correction.
Empirical experiments reported across 7 LLMs and 3 benchmark datasets (GSM8K, MATH, StrategyQA) comparing outcomes of iterative self-correction as a function of measured EIR.
Firms with a high market position tend to imitate the peer leader, whereas firms in middle and low market positions are more likely to follow the peer group.
Heterogeneity analysis / subgroup regressions in fixed-effects models on panel data of publicly listed Chinese firms (2012–2023), stratifying firms by market position (high, middle, low).
AI influences innovation performance in organizations.
Discussion and synthesis of studies and reports on AI adoption and innovation performance presented in the review.
AI adoption is producing organizational implications, including changes in project management practices.
Findings synthesized from conference papers, case studies and industry reports included in the review.
Automation, generative AI, and intelligent systems are reshaping task structures, leading to both job displacement risks and the creation of new AI-driven roles.
Synthesis of empirical studies, conference findings, and industry reports reporting both displacement risks and new role emergence (review paper).
AI is rapidly transforming the nature of work, the demand for skills, and the professional roles of Information Technology (IT) practitioners.
Stated as a synthesis result from a narrative review of recent empirical studies, conference findings, and industry reports (review paper).
Semiconductors are a representative case study for analyzing weaponized interdependence in advanced technology sectors.
Methodological claim in the paper: selection and focus on the semiconductor sector as illustrative of broader advanced-technology sector dynamics under export restraints and chokepoint activation.
Previous literature is based primarily on the short-term effectiveness of coercion; this paper shifts attention to the longer-term structural consequences of technological restraints.
Literature review and positioning in the paper contrasting prior studies' short-term focus with the paper's longer-term structural emphasis (methodological/literature-critique claim).
Over time, U.S.–China reaction–counterreaction interactions generate three structural transformations: supply-chain reconfiguration, substitution, and regulations reinforcing segmentation.
Synthesis from the paper's longitudinal/case-analysis of semiconductor-related export restraints and subsequent industry and regulatory responses (qualitative identification of three emergent structural outcomes).
Current instability in U.S.–China relations arises less from complete ideological divergence or failure of outright containment policy than from a structured reaction–counterreaction dynamic triggered by chokepoint activation.
Argument based on qualitative analysis of U.S. export restraints after the first Trump administration and application of the 'weaponized interdependence' framework to advanced-technology sectors (paper's theoretical argument and case discussion).
AIGC is reshaping the rights and obligations of platforms and workers.
Argument in the paper describing legal and practical impacts of AIGC on platform-worker relationships; based on doctrinal/legal analysis and discussion of platform practices rather than reported quantitative empirical data.
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
Survey evidence suggests public attitudes towards AI combine optimism with apprehension, and most respondents oppose granting AI systems final authority over hiring and dismissal decisions.
Review cites multiple public opinion and survey studies reporting mixed (optimistic and apprehensive) attitudes and opposition to AI final authority in employment decisions (survey evidence summarized).